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A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
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| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
rohitg00
/ llm-wiki.md
Last active
April 29, 2026 11:10
— forked from karpathy/llm-wiki.md
LLM Wiki v2 — extending Karpathy's LLM Wiki pattern with lessons from building agentmemory
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.
bretonium
/ gist:291f4388e2de89a43b25c135b44e41f0
Last active
April 29, 2026 11:10
- Shall i implement it?
- No ...
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| const Attribute = Pair{Symbol,<:Any} | |
| const BareElement = Tuple{Symbol,Vararg{Any}} | |
| const HTMLElement = Tuple{Symbol,Union{NamedTuple,Nothing},Vararg{Any}} | |
| const VoidElement = Tuple{Symbol,Union{NamedTuple,Nothing}} | |
| const VoidTags = (:area, :base, :br, :col, :embed, :hr, :img, :input, :link, :meta, :param, :source, :track, :wbr) | |
| escapehtml(text) = replace(text, '&' => "&", '<' => "<", ">" => ">", '"' => """, ''' => "'") | |
| kebabcase(symbol::Symbol) = replace(string(symbol), '_' => '-') | |
| struct Raw{Character} end |
| description | 4.1 Beast Mode | |||||||||||||||||||||||
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| tools |
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Beast Mode v3 is out now: 👉 https://gist.github.com/burkeholland/88af0249c4b6aff3820bf37898c8bacf
You are an agent - please keep going until the user’s query is completely resolved, before ending your turn and yielding back to the user.
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